Clinical Prediction of Functional Decline in Multiple Sclerosis Using Volumetry-Based Synthetic Brain Networks
Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS.
Authors
- Alin Ciubotaru (ORCID: https://orcid.org/0009-0003-2079-2383)
- Laura Riscanu
- Sebastian Cozma (ORCID: https://orcid.org/0000-0002-3768-6232)
- Alexandra Maștaleru (ORCID: https://orcid.org/0000-0002-0008-9696)
- Diana Lacatusu
- Albert Vamanu
- Cristiana Filip
- Laura-Elena Cucu (ORCID: https://orcid.org/0009-0000-1544-1325)
- Robert-Valentin Bilcu
- Florina Crivoi
- Sofia Alexandra Socolov-Mihaita
- Ioana Martu
- Oana-Roxana Bitere-Popa
- Thomas Gabriel Schreiner
- Lucia Corina Dima-Cozma
- Roxana Covali
Institutions
- Carol Davila University of Medicine and Pharmacy (RO)
- King's College London (GB)
- Clinical Emergency Hospital Bucharest (RO)
- Grigore T. Popa University of Medicine and Pharmacy (RO)
Publication Details
- Journal
- Life
- Published
- 2026-03-11
- DOI
- https://doi.org/10.3390/life16030459
- Primary Topic
- Multiple Sclerosis Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00